Machine Learning uses a number of statistical methods and techniques to create predictive models for classification, regression, clustering, manifold learning, density estimation and many other tasks. A machine-learned model summarizes the statistical relationships found in raw data and is capable of generalizing them to make predictions for new data points. Machine-learned models have been and are used for an extraordinarily wide variety of problems in science, engineering, banking, finance, marketing, and many other disciplines. Uses are truly limited only by the availability and quality of datasets. Building a model on a large dataset can take a long time. Further, the time and resources necessary to build a model increases as the required quality or depth of the model increases. In view of these investments, some models are valuable to other users. Datasets themselves also may have value in view of the investment to acquire, check or scrub, and store the dataset. In fact, many interesting datasets are built after laborious processes that merge and clean multiple sources of data. Often datasets are based on proprietary or private data that owners do not want to share in their raw format.